Ensemble Neural Network State Machine for Detecting Distractions

3. The vehicle device of claim 2, wherein a face and hand detection model is configured to detect the face and the hand of the user based at least in part on the sensor data, wherein the face and the hand of the user are identified based at least in part on the output of the face and hand detection model.

4. The vehicle device of claim 2, wherein a face and hand detection model is configured to detect the face of the user by identifying one or more face bounding boxes and detect the hand of the user by identifying one or more hand bounding boxes.

5. The vehicle device of claim 2, wherein the first model comprises a hand action classification model and the second model comprises a head pose classification model.

6. The vehicle device of claim 2, wherein the plurality of models further includes a fourth model configured to detect one or more eye gaze angles based at least in part on the face of the user, wherein the third model is further configured to predict the probability of the event further based at least in part on the one or more eye gaze angles.

7. The vehicle device of claim 2, wherein the ensemble neural network further comprises a plurality of layers, wherein the plurality of models are distributed across the plurality of layers.

8. The vehicle device of claim 7, wherein a first layer of the plurality of layers of the ensemble neural network comprises the first model and the second model and a second layer of the plurality of layers of the ensemble neural network comprises the third model.

9. The vehicle device of claim 2, wherein the sensor data comprises at least one of camera data, accelerometer data, audio data, or location data.

11. The vehicle device of claim 2, wherein, to trigger the event alert, the one or more processors are configured to execute the program instructions to further cause the vehicle device to trigger the event alert at a frame by frame level.

12. The vehicle device of claim 2, wherein the ensemble neural network comprises a frame classifier pipeline and a sequence detector pipeline.

13. The vehicle device of claim 2, wherein the event indicates a distracted state of the user.

14. The vehicle device of claim 2, wherein the one or more processors are configured to execute the program instructions to further cause the vehicle device to train the ensemble neural network.

15. The vehicle device of claim 2, wherein the second model is configured to detect the head pose based at least in part on detection of one or more of a yaw, a pitch, or a roll angle.

16. The vehicle device of claim 2, wherein the one or more hand actions comprise at least one of a neutral hand action, a hand interacting with a phone hand action, or a hand interacting with food hand action.

17. The vehicle device of claim 2, wherein the third model is configured to predict the probability of the event based at least in part on an output of the first model and an output of the second model.

18. The vehicle device of claim 2, wherein the plurality of models further includes a fourth model configured to detect a start time and an end time of the event based at least in part on the probability of the event.

19. The vehicle device of claim 2, wherein the sensor data comprises streaming sensor data.